While public and private entities question the utility of privacy preserving means of electronic payments for individuals other than criminals, discussions are primarily based on anecdotal evidence. Thus, we address the overall research question of whether pseudonymous cryptocurrencies are primarily used by criminals. Based on Rational Choice Theory and darknet market design, we build a dynamic research model. Utilizing panel data of 296,875 unique product and service listings that were available on 19 darknet markets from June 2014 to July 2015 as well as Bitcoin blockchain transactions, we provide evidence for the co-evolution of Bitcoin and darknet markets. We find that transactions within the Bitcoin blockchain and the usage of transaction obfuscation services can be related to previous sales on darknet markets. The temporal lag can be attributed to escrow mechanisms. We contribute to the research stream of cryptocurrency usage behavior and discussions of regulators, governments and financial services firms.

This exploratory study investigates how potential information technology security breaches affect stock prices. Previous research indicates that stock markets tend to punish firms that experience unsolicited disclosure of information and proprietary data. However, little research exists on the question of whether firms are punished for creating the mere potential for data theft. Based on the information boundary theory, we design our exploratory research model. Subsequently, we utilize a sample of 4,147 stocks of firms headquartered in 43 countries to conduct multiple event studies. We reveal a delayed adverse stock market response to potential IT security breaches as well as a discrimination among firms operating in different industries. Consequently, this work enhances the understanding of the full economic impact of information security measures by shedding light on previously neglected hidden costs.

This normative paper conceptualizes an alternative for the current scientific peer reviewing and publication system. Based on design science research methodology, we propose a distributed peer-to-peer network and transactional data base system (APOLLO) and a cryptocurrency (APL). We conceptualize a market for peer-to-peer reviewing and publishing of research contributions and associated assets that is based on economic market mechanisms and does not require centralized authorities or intermediaries. We discuss how the resulting decentralized and pseudonymous market for research assets could help to mitigate unresolved conflicts of interests and biases prevalent in the current system.

This paper conceptualizes the decentralized information systems success model. Based on theoretical and empirical findings in the realm of Information Systems (IS) and Open Source Software (OSS) acceptance and success research as well as various IS enabled socio-economical trends, we define ten constructs (societal norms, economic boundaries, intention to contribute, intention to use, objective quality, heuristic/perceived quality, level of contribution, level of usage, intellectual net benefit and economic net benefit) as well as their relationships. To enhance the understanding of the proposed constructs and relationships, we give examples of the contribution to and the usage of cryptocurrencies. We contribute to the IS research stream of IS acceptance and success by providing a model that allows for an effective examination of decentralized IS success.

We examine whether the popular 2/3 rule-of-thumb splitting criterion used in out-of-sample evaluation of predictive econometric and machine learning models makes sense. We conduct simulations regarding the predictive performance of the logistic regression and decision tree algorithm when considering varying splitting points as well as sample sizes. Our non-exhaustive repeated random sub-sampling simulation approach known as Monte Carlo cross-validation indicates that while the 2/3 rule-of-thumb works, there is a spectrum of different splitting proportions that yield equally compelling results. Furthermore, our results indicate that the size of the complete sample has little impact on the applicability of the 2/3 rule-of-thumb. However, our analysis reveals that when considering relatively small and relatively large training samples in relation to the sample size, the variation of the predictive accuracy can lead to misleading results. Our results are especially important for IS researchers considering the usage of out-of-sample methods for evaluating their predictive models.

User-generated online reviews are an important decision aid for consumers affecting purchase prob-abilities and sales figures. However, little is known about factors influencing the review generation process. Thus, this paper empirically examines the impact of cross-organizational spillover effects on user-generated online service reviews. Specifically, we study how the overall perception of consumers towards a service provider expressed in online reviews is affected by upstream service providers in interdependent service chains (ISCs). Based on the Treatment-By-Association (TBA) phenomenon, we design a research model to study both the existence and evolvement of cross-organizational spillover effects in online reviews of ISCs. Utilizing every airline and airport review posted over 13 years on www.AirlineQuality.com, we show that both positive and negative spillover effects exist: Increased (decreased) overall ratings of an upstream service node are associated with increased (decreased) ratings of the directly following service node in the ISC. In addition, we show that this is not true for more distantly arranged service nodes. We contribute to the IS research stream of online reviews by shedding light on spillover effects and by providing evidence for the proposed TBA. Furthermore, suggestions how practitioners could manage and utilize spillover effects to improve their customer experience are provided.

This paper provides first empirical evidence on the impact of reviewer status on the objectivity of his contributions in online communities. While previous research indicates that user-generated online reviews guide consumer decision making, little is known about drivers of the actual review generation process. By drawing on Functional Role Theory, we derive four research hypotheses covering the general research question of factors influencing the objectivity of service reviews. Utilizing a data sample covering 413,077 reviews posted over 12 years on www.TripAdvisor.com, we evaluate our research model. Our findings indicate that with increased user status, review objectivity increases. Thus, we contribute to theory by generalizing the so-called "Popularity Effect" to a multi-dimensional "Status Effect", which is more widely applicable (e.g. settings without users-follow-users relationships). Furthermore, our results enable practitioners to find their most valuable content-producers.

Reference No.: 2015-84

Presentations:

2017

11. August

Janze, C.

Are Cryptocurrencies Criminals Best Friends? Examining the Co-Evolution of Bitcoin and Darknet Markets